Bayesian $M$ -Ary Hypothesis Testing: The Meta-Converse and Verdú-Han Bounds Are Tight PROJECT TITLE :Bayesian $M$ -Ary Hypothesis Testing: The Meta-Converse and Verdú-Han Bounds Are TightABSTRACT:Two various precise characterizations of the minimum error likelihood of Bayesian -ary hypothesis testing are derived. The first expression corresponds to the error likelihood of an induced binary hypothesis check and implies the tightness of the meta-converse sure by Polyanskiy et al.; the second expression could be a perform of an information-spectrum measure and implies the tightness of a generalized Verdú-Han lower sure. The formulas characterize the minimum error likelihood of several issues in info theory and facilitate to spot the steps where existing converse bounds are loose. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Exploit Every Bit: Effective Caching for High-Dimensional Nearest Neighbor Search Tunably Rugged Landscapes With Known Maximum and Minimum
